Data mining and machine learning algorithms and methods in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis), and in new areas
Mining text and semi-structured data, and mining temporal, spatial and multimedia data
Mining data streams
Mining spatio-temporal data
Mining with data clouds and Big Data
Link and graph mining
Pattern recognition and trend analysis
Collaborative filtering/personalization
Data and knowledge representation for data mining
Query languages and user interfaces for mining
Complexity, efficiency, and scalability issues in data mining
Data pre-processing, data reduction, feature selection and feature transformation
Post-processing of data mining results
Statistics and probability in large-scale data mining
Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
Integration of data warehousing, OLAP and data mining
Human-machine interaction and visual data mining
High performance and parallel/distributed data mining
Quality assessment and interestingness metrics of data mining results
Visual Analytics
Security, privacy and social impact of data mining
Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance, marketing, healthcare, telecommunications and other fields
